Hossein Hashemi
Researcher
APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies
Author
Summary, in English
One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs. To solve this problem, this study develops the Absence Point Generation (APG) toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies. The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude, slope degree, topographic wetness index, and distance from rivers, and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are generated. To test the APG toolbox, we applied two benchmark algorithms of random forest (RF) and boosted
regression trees (BRT) in a case study to investigate groundwater potential using three absence datasets i.e., the APG, random, and selection of absence samples (SAS) toolbox. The BRT-APG and RF-APG had the area under receiver operating curve (AUC) values of 0.947 and 0.942, while BRT and RF had weaker performances with the SAS and Random datasets. This effect resulted in AUC improvements for BRT and RF
by 7.2, and 9.7% from the Random dataset, and AUC improvements for BRT and RF by 6.1, and 5.4% from the SAS dataset, respectively. The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps, which proves the importance of absence points in environmental binary issues. The proposed APG toolbox could be easily applied in other environmental hazards such as
landslides, floods, and gully erosion, and land subsidence.
regression trees (BRT) in a case study to investigate groundwater potential using three absence datasets i.e., the APG, random, and selection of absence samples (SAS) toolbox. The BRT-APG and RF-APG had the area under receiver operating curve (AUC) values of 0.947 and 0.942, while BRT and RF had weaker performances with the SAS and Random datasets. This effect resulted in AUC improvements for BRT and RF
by 7.2, and 9.7% from the Random dataset, and AUC improvements for BRT and RF by 6.1, and 5.4% from the SAS dataset, respectively. The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps, which proves the importance of absence points in environmental binary issues. The proposed APG toolbox could be easily applied in other environmental hazards such as
landslides, floods, and gully erosion, and land subsidence.
Department/s
- Division of Water Resources Engineering
- LTH Profile Area: Water
- Centre for Advanced Middle Eastern Studies (CMES)
- MECW: The Middle East in the Contemporary World
Publishing year
2021-06
Language
English
Publication/Series
Geoscience Frontiers
Volume
12
Issue
6
Full text
Document type
Journal article
Publisher
China University of Geosciences (Beijing) and Peking University
Topic
- Water Engineering
Status
Published
ISBN/ISSN/Other
- ISSN: 1674-9871